text: // Copyright 2018 The MACE Authors. All Rights Reserved.
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //     http://www.apache.org/licenses/LICENSE-2.0
  //
  // Unless required by applicable law or agreed to in writing, software
  // distributed under the License is distributed on an "AS IS" BASIS,
  // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  // See the License for the specific language governing permissions and
  // limitations under the License.
  
  #include "mace/ops/opencl/buffer/conv_2d.h"
  
  namespace mace {
  namespace ops {
  namespace opencl {
  namespace buffer {
  
  bool Conv2dKernel::CheckUseWinograd(
      OpenclExecutor *executor,
      const std::vector<index_t> &filter_shape,
      const std::vector<index_t> &output_shape,
      const int *strides,
      const int *dilations,
      int *wino_block_size) {
    MACE_UNUSED(kwg_size_);
    MACE_UNUSED(executor);
    MACE_UNUSED(output_shape);
    MACE_UNUSED(wino_block_size);
    return (filter_shape[2] == 3 && filter_shape[3] == 3 &&
        strides[0] == 1 && strides[1] == 1 &&
        dilations[0] == 1 && dilations[1] == 1);
  }
  
  MaceStatus Conv2dKernel::Compute(
      OpContext *context,
      const Tensor *input,
      const Tensor *filter,
      const Tensor *bias,
      const int *strides,
      const Padding &padding_type,
      const std::vector<int> &padding_data,
      const int *dilations,
      const ActivationType activation,
      const float relux_max_limit,
      const float activation_coefficient,
      const int winograd_blk_size,
      Tensor *output) {
    MACE_UNUSED(winograd_blk_size);
    StatsFuture pad_future, conv_future;
    index_t filter_h = filter->dim(2);
    index_t filter_w = filter->dim(3);
    // Reshape output
    std::vector<index_t> output_shape(4);
    std::vector<int> paddings(2);
    if (padding_data.empty()) {
      ops::CalcNHWCPaddingAndOutputSize(
          input->shape().data(), filter->shape().data(), dilations, strides,
          padding_type, output_shape.data(), paddings.data());
    } else {
      paddings = padding_data;
      CalcOutputSize(input->shape().data(), filter->shape().data(),
                     padding_data.data(), dilations, strides, RoundType::FLOOR,
                     output_shape.data());
    }
  
    MACE_RETURN_IF_ERROR(output->Resize(output_shape));
  
    // calculate padded input shape
    index_t width = output_shape[2];
    index_t channels = output_shape[3];
  
    index_t input_height = input->dim(1);
    index_t input_width = input->dim(2);
    index_t input_channels = input->dim(3);
  
    int pad_top = paddings[0] >> 1;
    int pad_left = paddings[1] >> 1;
  
    MACE_CHECK(filter->dim(0) == channels, filter->dim(0), " != ", channels);
    MACE_CHECK(filter->dim(1) == input_channels, filter->dim(1), " != ",
               input_channels);
  
    std::function<MaceStatus(const Tensor *input, Tensor *output)> conv_func;
  
    // Mark whether input changed or not
    bool input_changed = IsResetArgsNeeded(context, input_shape_, input->shape());
    input_shape_ = input->shape();
  
    bool use_1x1 = filter_h == 1 && filter_w == 1;
  
    std::vector<index_t> padded_output_shape = output_shape;
    index_t tile_w, tile_c = 4;
    if (use_1x1) {
      tile_w = 2;
    } else {
      tile_w = 4;
    }
    padded_output_shape[2] = RoundUp<index_t>(width, tile_w);
  
    std::vector<index_t> padded_input_shape = input->shape();
    padded_input_shape[1] = input_height + paddings[0];
    padded_input_shape[2] = (padded_output_shape[2] - 1) * strides[1] +
        (filter_w - 1) * dilations[1] + 1;
    padded_input_shape[3] = RoundUp<index_t>(input_channels, tile_c);
  
    const Tensor *padded_input_ptr = input;
    // pad input
    std::unique_ptr<Tensor> padded_input;
    if (padded_input_shape[1] != input_height ||
        padded_input_shape[2] != input_width ||
        padded_input_shape[3] != input_channels) {
      // decide scratch size before allocate it
      index_t padded_input_size =
          std::accumulate(padded_input_shape.begin(),
                          padded_input_shape.end(),
                          1, std::multiplies<index_t>()) +
              MACE_EXTRA_BUFFER_PAD_SIZE / GetEnumTypeSize(input->dtype());
      auto *runtime = context->runtime();
      padded_input.reset(new Tensor(
          runtime, input->dtype(), output->memory_type(), {padded_input_size}));
      runtime->AllocateBufferForTensor(padded_input.get(), RENT_SCRATCH);
      padded_input->Resize(padded_input_shape);
      PadInput(context, &kernels_[0], input, pad_top, pad_left,
               input_changed, padded_input.get(), &pad_future);
      padded_input_ptr = padded_input.get();
    }
  
    if (use_1x1) {
      conv_func = [&](const Tensor *pad_input, Tensor *output) -> MaceStatus {
        return conv2d::Conv2d1x1(
            context, &kernels_[1], pad_input, filter, bias, strides,
            activation, relux_max_limit,
            activation_coefficient, input_changed, output, &conv_future);
      };
    } else {
      conv_func = [&](const Tensor *pad_input, Tensor *output) -> MaceStatus {
        return conv2d::Conv2dGeneral(
            context, &kernels_[1], pad_input, filter, bias, strides, dilations,
            activation, relux_max_limit,
            activation_coefficient, input_changed, output, &conv_future);
      };
    }
    MACE_RETURN_IF_ERROR(conv_func(padded_input_ptr, output));
    MergeMultipleFutureWaitFn({pad_future, conv_future}, context->future());
  
    return MaceStatus::MACE_SUCCESS;
  }
  
  }  // namespace buffer
  }  // namespace opencl
  }  // namespace ops
  }  // namespace mace
  